Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
600_03_Quarto
1 Report on analysis of Iris datasets
1.1 Tabsets
1.2 Exploring data
Based on the group selected setosa, here is the table data.
And calculating for Species setosa and number of observations 50.
1.3 1. Checking custom fits
This is a custom function fit:
Call:
lm(formula = Petal.Length ~ Petal.Width, data = d)
Residuals:
Min 1Q Median 3Q Max
-0.43686 -0.09151 -0.03686 0.09018 0.46314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.32756 0.05996 22.141 <2e-16 ***
Petal.Width 0.54649 0.22439 2.435 0.0186 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1655 on 48 degrees of freedom
Multiple R-squared: 0.11, Adjusted R-squared: 0.09144
F-statistic: 5.931 on 1 and 48 DF, p-value: 0.01864
1.4 Running multiple regression
You can add options to run multiple regression
Code
library(tidyverse)
d <- iris %>% select(Petal.Length, Petal.Width, Sepal.Length, Sepal.Length)
fit <- lm(Petal.Width ~ Sepal.Length + Sepal.Length + Petal.Length, data = d)
# Obtain predicted and residual values
d$predicted <- predict(fit)
d$residuals <- residuals(fit)
ggplot(d, aes(x = Sepal.Length, y = Petal.Width)) +
geom_segment(aes(xend = Sepal.Length, yend = predicted), alpha = .2) + # Lines to connect points
geom_point() + # Points of actual values
geom_point(aes(y = predicted), shape = 1) + # Points of predicted values
theme_bw()1.5 Adding multiple predictors on graph:
1.6 Running multiple different code
1.6.1 Example with R
Here with using R Language.
1.6.2 Example with Python
Example with Python
And overall it is irrelevant the origin of language. and mixing the languages
1.6.3 Math
some formula f(x) = a*b^{3} + 4
Some formula \sqrt{2x + 4^{3}}
Quadratic formula (or Sridharacharya formula) for second-order polynomial equation x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
Bayes rules: Pr(\theta | y) = \frac{Pr(y | \theta) Pr(\theta)}{Pr(y)}
1.7 Adding additional table with gt() asis does not work. use it with html
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
|---|---|---|---|---|---|---|---|---|---|---|
| 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| 22.8 | 4 | 108 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
2 Conclusion
The results show bigger residuals and predicting the multiple variate regression without filtering the species, to be “interesting” idea.
This analysis is fictitious and does not provide any real results